Traditional stats miss the “how” behind yards gained
Previous research has explored athletes reaching theoretical max capacity 1
Can we “measure” effort using tracking data?
Game, play, player, tracking data from Weeks 1-9 1
Running plays where a running back (RB) is the ball carrier
Trimmed each play to frames between handoff and end of play
Biased toward backups because of lower sample size…or are they just working harder? Are starters fatigued or pacing themselves?
Unrealistic theoretical max speeds - not comparable to soccer
Effort v1
\[ \left(\sum\limits_{i=1}^{n_{\text{below}}} {\frac{1}{1+d_i}}\right)\bigg/n_{\text{below}} \] ➜️ Quantifies how close a player comes to his “best” (99th percentile) accelerations
➜ Saquon Barkley: 0.152
➜ Rex Burkhead: 0.149
Effort v2
Percentage of total points that lie in between the percentile \(P_{99}\) and \(P_{99}-3\)
➜ Quantifies how often a player comes close to his “best” (99th percentile) accelerations
➜ Saquon Barkley: 0.074
➜ Rex Burkhead: 0.069
Unsure about threshold for relaxed percentile line in Effort v2
Problem with qgam: some players have more than 1% of points above 99th percentile
Define research question and scope ✅
Data cleaning and preprocessing, EDA ✅
Develop basic AS profiles for players ✅
Obtain effort metric(s) with quantile regression ⏳
Evaluate effort metric(s) by correlating it to effort-related outcomes